Some search engines log which documents are clicked for particular queries. These query click logs are a rich source of information in the search business, because they reflect users' preference over the documents presented by a search engine. In addition to being first-party judgments, click logs are useful because they easily outweigh human judges' third-party judgments in terms of both query coverage and quantity. One way to leverage query click logs is to infer the user-perceived relevance of each document with respect to the query that elicited the document. However, while valuable for many applications, inference of document relevance from user clicks is a difficult problem to solve. There may be numerous ad hoc approaches to deriving document relevance, e.g., the more clicks a document receives the more relevant it is, but the effectiveness of such ad hoc approaches is limited because of the lack of theoretical foundations, unpredictable performance in reality, and maintenance difficulties. What is needed is a principled approach to inferring document relevance from user click logs.
Techniques related to click chain modeling are described below.